Some misinterpretations of Inferential statistics in dental public health literature
Bibliographic record
Abstract
OBJECTIVES: Inferential statistics such as p-values and confidence intervals (CIs) are ubiquitously used in research studies. Still, researchers can incorrectly interpret them, impacting the validity and utility of the respective results. The aim was to quantify how often studies commit one incorrect interpretation, dichotomization, in the dental public health literature. METHODS: The authors carried out an electronic search using PubMed to extract original papers published in 2018/2019/2023 (either online or in print) in five dental public health journals. Four trained and calibrated reviewers extracted information from the abstract and main text on the following: reporting any p-value (Yes/No), reporting p-value as inequality (Yes/No/NA), reporting non-significant p-value (Yes/No/Not applicable (NA)), reporting any confidence interval (Yes/No), reporting non-significant confidence interval (Yes/No/NA), and concluding there is no association because the p-value or confidence intervals were not-significant (Yes/No). In addition, investigators extracted if studies explicitly reported that p < 0.05 was considered significant. RESULTS: The results indicate that p-values were reported more frequently than CIs in both the abstract and main text. The majority of studies interpreted non-significant p-values or CIs to mean no association or no effect in their main text. Also, non-significant p-values and CIs were less frequently reported in abstracts compared to the main text. These findings varied across the five dental public health journals, but were less notably changed by the print publication year. CONCLUSION: The results of this paper showed that some common misconceptions about p-values and CIs still linger in the dental public health literature after seven years had passed since warnings against such practices. More advanced training may be needed to overcome the issues with p-values, confidence interval reporting, and interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".